For the purposes of ML model training, patient outcomes are often defined based on laboratory/diagnostic test results extracted from the electronic health record (EHR; e.g. [1]}, [2]}, [3]}), since clinical chart review on large patient databases can be prohibitively costly.
In doing so, many researchers assign “negative" labels to untested patients (the negativity assumption in positive-unlabeled learning [4]}). For example, many sepsis prediction models derive labels from laboratory test-based definitions, such that untested patients are negative by definition [5]}, [6]}, [7]}, [8]}.
Beyond sepsis, this is also the case when building models to predict healthcare-associated infections [9]}, [10]}, [11]}. Researchers typically justify this assumption, since without it, a model trained on only patients who were tested may only apply to the small fraction of tested patients, limiting its utility.
